Fundamental concepts of qualitative probabilistic networks
Artificial Intelligence
Real-world applications of Bayesian networks
Communications of the ACM
Bayesian belief networks for safety assessment of computer-based systems
System performance evaluation
Software Measurement: Uncertainty and Causal Modeling
IEEE Software
Building Probabilistic Networks: 'Where Do the Numbers Come From?' Guest Editors' Introduction
IEEE Transactions on Knowledge and Data Engineering
Network Engineering for Agile Belief Network Models
IEEE Transactions on Knowledge and Data Engineering
Dealing with the Expert Inconsistency in Probability Elicitation
IEEE Transactions on Knowledge and Data Engineering
Probability elicitation for belief networks: issues to consider
The Knowledge Engineering Review
Building large-scale Bayesian networks
The Knowledge Engineering Review
Making Resource Decisions for Software Projects
Proceedings of the 26th International Conference on Software Engineering
Predicting software defects in varying development lifecycles using Bayesian nets
Information and Software Technology
Multiplicative factorization of noisy-max
UAI'99 Proceedings of the Fifteenth conference on Uncertainty in artificial intelligence
Elicitation of probabilities for belief networks: combining qualitative and quantitative information
UAI'95 Proceedings of the Eleventh conference on Uncertainty in artificial intelligence
Object-oriented Bayesian networks
UAI'97 Proceedings of the Thirteenth conference on Uncertainty in artificial intelligence
Network fragments: representing knowledge for constructing probabilistic models
UAI'97 Proceedings of the Thirteenth conference on Uncertainty in artificial intelligence
Efficient search-based inference for noisy-OR belief networks: topepsilon
UAI'96 Proceedings of the Twelfth international conference on Uncertainty in artificial intelligence
Network engineering for complex belief networks
UAI'96 Proceedings of the Twelfth international conference on Uncertainty in artificial intelligence
Probabilities for a probabilistic network: a case study in oesophageal cancer
Artificial Intelligence in Medicine
On the effectiveness of early life cycle defect prediction with Bayesian Nets
Empirical Software Engineering
A socio-technical approach to business process simulation
Decision Support Systems
PROMISE '09 Proceedings of the 5th International Conference on Predictor Models in Software Engineering
Bayesian network based business information retrieval model
Knowledge and Information Systems
Assessing critical success factors for military decision support
Expert Systems with Applications: An International Journal
Causal networks for risk and compliance: methodology and application
IBM Journal of Research and Development
Information and Software Technology
The use of application scanners in software product quality assessment
Proceedings of the 8th international workshop on Software quality
A framework for integrated software quality prediction using Bayesian nets
ICCSA'11 Proceedings of the 2011 international conference on Computational science and Its applications - Volume Part V
An ontology-based approach for constructing Bayesian networks
Data & Knowledge Engineering
A conceptual Bayesian net model for integrated software quality prediction
Annales UMCS, Informatica
Building an expert-based web effort estimation model using bayesian networks
EASE'09 Proceedings of the 13th international conference on Evaluation and Assessment in Software Engineering
pi-football: A Bayesian network model for forecasting Association Football match outcomes
Knowledge-Based Systems
Expert Systems with Applications: An International Journal
A model to detect problems on scrum-based software development projects
Proceedings of the 28th Annual ACM Symposium on Applied Computing
Using evidential reasoning to make qualified predictions of software quality
Proceedings of the 9th International Conference on Predictive Models in Software Engineering
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Although Bayesian Nets (BNs) are increasingly being used to solve real world risk problems, their use is still constrained by the difficulty of constructing the node probability tables (NPTs). A key challenge is to construct relevant NPTs using the minimal amount of expert elicitation, recognising that it is rarely cost-effective to elicit complete sets of probability values. We describe a simple approach to defining NPTs for a large class of commonly occurring nodes (called ranked nodes). The approach is based on the doubly truncated Normal distribution with a central tendency that is invariably a type of weighted function of the parent nodes. In extensive real-world case studies we have found that this approach is sufficient for generating the NPTs of a very large class of nodes. We describe one such case study for validation purposes. The approach has been fully automated in a commercial tool, called AgenaRisk, and is thus accessible to all types of domain experts. We believe this work represents a useful contribution to BN research and technology since its application makes the difference between being able to build realistic BN models and not.